With the increasing use of computers and computerized technology, the amount of information represented digitally has become enormous. Analysis of these vast quantities of digital data generally involves the recognition of known patterns.
Traditionally, information originating in a digital format is ultimately analyzed through manual review by a person who often requires substantial training. In order for people to efficiently interact with volumes of digital data, the information must typically be converted into a visual, audible, or other human-perceivable representation. However, during the process of translating digital data from its raw form into a convenient output format, some information may be lost or misinterpreted. Moreover, the data is often processed and/or filtered for presentation prior to analysis thereby resulting in the loss of significant information from the original data. While humans can be trained to analyze many different types of data, manual human analysis is generally more expensive with regard to time and accuracy than automated systems. Additionally, errors are often introduced due to the inherent limitations of human perception and attention span. Frequently, the data contains more detail than human senses can discern, and it is documented that human repetition begets errors.
To address the innate shortcomings of human analysis, many automated data analysis and pattern recognition systems have been developed and subsequently improved upon. However, most of these solutions are highly data-specific and/or processing intensive. The data inputs that a pattern recognition system can handle are often fixed and limited by design such that applicability is restricted to a specific data modality; otherwise stated, the system by which the data is evaluated is tightly coupled to the specific data source it is designed to evaluate. Hence, improvements across a broad range of systems are very difficult.
Furthermore, within many systems, pattern and feature recognition is processing-intensive. For example, image analysis commonly uses complex algorithms to find geometric shapes, edges, etc., for the purpose of characterizing or classifying features of interest; this requires multitudes of algorithms to be processed. The time to discover, develop, and implement each algorithm causes an incremental delay in deploying or improving the system.
Thus, there still remains substantial room for improvement in the arena of automated data analysis and pattern recognition systems.